class_docstr: Base class for seq2seq models with embeddings
    
docstr: Returns the embedding used for the source sequence.
      
docstr_tok: ["Returns","the","embedding","used","for","the","source","sequence","."]
code: def source_embedding(self):
      """Returns the embedding used for the source sequence."""
      return tf.get_variable(
          name="W",
          shape=[
              self.source_vocab_info.total_size,
              self.params["embedding.dim"],
          ],
          initializer=tf.random_uniform_initializer(
              -self.params["embedding.init_scale"],
              self.params["embedding.init_scale"],
          ),
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import collections
  import tensorflow
  from seq2seq import graph_utils
  from seq2seq import losses
  from seq2seq.contrib.seq2seq.decoder import _transpose_batch_time
  from seq2seq.data import vocab
  from seq2seq.graph_utils import templatemethod
  from seq2seq.decoders.beam_search_decoder import BeamSearchDecoder
  from seq2seq.inference import beam_search
  from seq2seq.models.model_base import ModelBase, _flatten_dict
label_desc: Use image node_id 6 for calling the Seq2SeqModel obj's underlying member method code with example usage: obj.source_embedding() and returns: tf